Tumor segmentation and tissue classification in 3d multi-contrast

ABSTRACT

A medical imaging system ( 5 ) includes a workstation ( 20 ), a coarse segmenter ( 30 ), a fine segmenter ( 32 ), and an enclosed tissue identification module ( 34 ). The workstation ( 20 ) includes at least one input device ( 22 ) for receiving a selected location as a seed in a first contrasted tissue type and a display device ( 26 ) which displays a diagnostic image delineating a first segmented region of a first tissue type and a second segmented region of a second contrasted tissue type and identified regions which include regions fully enclosed by the first segmented region as a third tissue type. The coarse segmenter ( 30 ) grows a coarse segmented region of coarse voxels for each contrasted tissue type from the seed location based on a first growing algorithm and a growing fraction for each contrasted tissue type. The seed location for growing the second contrasted tissue type includes the first coarse segmented region and any fully enclosed coarse voxels, and each coarse voxel includes an aggregation of voxels and a maximum and a minimum of the voxel intensities. The fine segmenter ( 32 ) grows a segmented region of voxels for each contrasted tissue type from the seed location and bounded by the second coarse segmented region based on a second growing algorithm and a growing fraction for each contrasted tissue type initially set to the growing fraction for the corresponding region. The seed location for growing the second contrasted tissue type includes the first segmented region and any identified regions. The enclosed tissue identification module ( 34 ) identifies any regions of voxels fully enclosed by the first segmented region as being of the third tissue type. The coarse segmenter, the fine segmenter, and the enclosed tissue identification module are implemented by an electronic data processing device.

This application is a continuation of U.S. patent application Ser. No.14/374,652, filed Jul. 25, 2014, which is the U.S. National Phaseapplication under 35 U.S.C. §371 of International Application No.PCT/IB2013/050644, filed on Jan. 25, 2013, which claims the benefit ofU.S. Provisional Patent Application No. 61/591,396, filed on Jan. 27,2012. These applications are hereby incorporated by reference herein.

The following relates generally to medical imaging. It finds particularapplication in conjunction with segmenting tissue types in diagnosticimaging, and will be described with particular reference thereto.However, it will be understood that it also finds application in otherusage scenarios and is not necessarily limited to the aforementionedapplication.

A frequent application of medical imaging is tumor diagnosis andgrading. In grading a tumor, segmentation is used to identify vitaltumor tissue, surrounding edema tissue, and any necrotic tissuecompletely enclosed by the vital tumor tissue. Tumor segmentationdiffers from organ segmentation which is facilitated by a model-basedapproach. A model-based approach works for organ segmentation becausethe anatomy of the body can be used as a guide. For example, manymodel-based approaches use an atlas in the segmentation process.However, tumors do not have a common form and do not have a common greyvalue structure. There are no anatomical models for tumors and thereforetumor segmentation cannot be based on anatomical models or a model-basedapproach.

Some algorithms for tumor segmentation attempt to differentiate tissuewith boundary identification. Typically, these segmentation approachesuse manual approaches based on geometric models or down sampling.Geometric models or down sampling use averages or interpolation for fastcomputational measurement of tumor boundaries which lose information,especially edge information. The edge information is important insegmenting the different tissue types involved with tumor stagingidentification such as vital tumor tissue, edema or peri-focal tissue,and necrotic tissue. The differentiation of tissue types is important inradiation therapy planning, surgical planning, tumor diagnosis andstaging, evaluation of treatment outcome via volumetry, automatic regionof interest (ROI) segmentation for quantitative analysis, etc. Forexample, radiation treatment typically focuses on vital tumor tissue.Necrotic tissue does not need radiation treatment because necrotictissue is dead tissue. However, radiation treatment does not need toavoid necrotic tissue. Radiation treatments typically avoid peri-focaltissue which is at risk, but still viable tissue.

Another approach uses a growing algorithm with an adaptive upper bound.The upper bound is the grey value passed by the growing region, and thelower bound is chosen relative to the upper bound. However, controllingthe growth to prevent leakage to small connecting bright bands ofneighboring structures is problematic.

Additionally multiple images may be needed to contrast the differenttumor tissue types. For example, an image with one contrast highlightsvital tumor tissue while an image with a different contrast highlightsedema tissue. Segmenting vital tumor tissue from edema tissue fromnormal tissue can be a difficult process, and local conditions canaffect the contrast of the tissue types which is used for segmentation.

The following discloses a new and improved tumor segmentation and tissueclassification which addresses the above referenced matters, and others.

In accordance with one aspect, a medical imaging system includes aworkstation, a coarse segmenter, a fine segmenter, and an enclosedtissue identification module. The workstation includes at least oneinput device for receiving a selected location as a seed in a firstcontrasted tissue type and a display device which displays a diagnosticimage delineating a first segmented region of a first tissue type and asecond segmented region of a second contrasted tissue type andidentified regions which include regions fully enclosed by the firstsegmented region as a third tissue type. The coarse segmenter grows acoarse segmented region of coarse voxels for each contrasted tissue typefrom the seed location based on a first growing algorithm and a growingfraction for each contrasted tissue type. The seed location for growingthe second contrasted tissue type includes the first coarse segmentedregion and any fully enclosed coarse voxels, and each coarse voxelincludes an aggregation of voxels and a maximum and a minimum of thevoxel intensities. The fine segmenter grows a segmented region of voxelsfor each contrasted tissue type from the seed location and bounded bythe second coarse segmented region based on a second growing algorithmand a growing fraction for each contrasted tissue type initially set tothe growing fraction for the corresponding region. The seed location forgrowing the second contrasted tissue type includes the first segmentedregion and any identified regions. The enclosed tissue identificationmodule identifies any regions of voxels fully enclosed by the firstsegmented region as being of the third tissue type. The coarsesegmenter, the fine segmenter, and the enclosed tissue identificationmodule are implemented by an electronic data processing device.

In accordance with another aspect, a method performs a coarse regiongrowing algorithm to define a coarse segmented region in an image, thecoarse region growing algorithm operating on coarse voxels of the imagewhere each coarse voxel is an aggregation of voxels characterized in thecoarse region growing algorithm by the maximum voxel value of theaggregation of voxels and the minimum voxel value of the aggregation ofvoxels. A fine region growing algorithm is performed to define asegmented region in the image, the fine region growing algorithmoperating on voxels of the image and being constrained by a growth bounddefined by the coarse segmented region. The segmented region isdisplayed on a display device. The performing operations are performedby an electronic data processing device.

In accordance with another aspect, a medical imaging system includes anelectronic data processing device configured to perform operationsincluding segmenting a T1 contrast enhanced (T1CE) magnetic resonanceimage of an imaging subject to define a segmented region comprisingvital tumor tissue, and segmenting a fluid attenuated inversion recovery(FLAIR) magnetic resonance image of the imaging subject using regiongrowing to define a segmented region comprising edema tissue. Thesegmented region comprising vital tumor tissue serves as the seed forthe region growing.

One advantage is segmentation which retains tissue edge information.

Another advantage is the masking of regions of segmentation to preventleakage of similarly contrasted, but unrelated tissue types.

Another advantage resides in the segmentation and identification ofrelated tumor tissue types.

Another advantage resides in a user interface for fine tuning of thesegmented tissue.

Still further advantages of the present application will be appreciatedto those of ordinary skill in the art upon reading and understanding thefollowing detailed description.

FIG. 1 schematically illustrates an embodiment of the medical imagingsystem.

FIG. 2A-B schematically illustrates several examples of coarse voxelconstruction performed by the coarse segmenter.

FIG. 3 diagrammatically illustrates an example of the various segmentedand identified regions.

FIG. 4 illustrates an example user interface.

FIG. 5 flowcharts one method of using an embodiment of the segmentationand tissue identification.

FIG. 6 flowcharts one method of the coarse segmentation growingalgorithm.

FIG. 7 flowcharts one method of the fine segmentation growing algorithm.

The invention may take form in various components and arrangements ofcomponents, and in various steps and arrangement of steps. The drawingsare only for purposes of illustrating the preferred embodiments and arenot to be construed as limiting the invention.

FIG. 1 schematically illustrates an embodiment of the medical imagingsystem 5. One or more medical images are generated from one or moremedical imaging devices 10 such as magnetic resonance (MR), positronemission tomography (PET) and the like. The medical images are stored ina data store 12. The medical images can be 2D such as 2D slices of avolume, 3D volumes, and the like. The images are spatially registered.For example, a global rigid 3D transformation is used for intracranialimaging. The data store can be local memory, disk, network attachedstorage, and the like. The one or more medical images includes a firstcontrast 14 indicative of a tumor tissue such as vital tumor tissue, anda second contrast 16 indicative of related tumor tissue such asperi-focal tissue. For example, MR T1 Contrast Enhanced (T1CE) contrastsvital tumor tissue as hyper-intense and MR Fluid Attenuated InversionRecovery (FLAIR) contrasts perifocal edema as hyper-intense. Other MRprotocols and contrasts include T1, T2, Magnetization of Prepared RapidGradient Echo (MPRAGE), Vascular Space Occupancy (VASO), and the like.The data store and medical imaging device connect by a network 18. Thenetwork can be direct or indirect, wired or wireless, private or publicsuch as the Internet, or any combination.

A workstation 20 connects to the network 18 and a healthcarepractitioner selects a seed or starting point from the first contrastusing at least one input device 22. The workstation 20 includes anelectronic processor or electronic processing device 24, a display 26which displays the first and/or second contrast, menus, panels, and usercontrols, and the at least one input device 22 which inputs thehealthcare practitioner selections. The workstation 20 can be a desktopcomputer, a laptop, a tablet, a mobile computing device, a smartphone,and the like. The input device can be a keyboard, a mouse, a microphone,and the like. The seed location selected on the displayed contrast isconverted by a user interface module 28 to a voxel location.

The user interface module 28 processes healthcare practitioner input viaat least one input device relative to the display displayed by thedisplay device. The displays can include menus, panels, web pages, usercontrols, parameters selections, and the like.

A coarse segmenter 30 creates coarse voxels which include an aggregationof voxels and a maximum and a minimum of the voxel intensities of theaggregated voxels. The coarse segmenter 30 grows a coarse segmentedregion of coarse voxels for each contrasted tissue type from the seedlocation based on a growing algorithm and a growing fraction for eachcontrast. The algorithm iterates with a starting growing fraction for acoarse segmented region and increments the growing fraction until thegrown region contains the seed. The seed location for the secondcontrasted tissue type includes the coarse segmented region for thefirst contrasted tissue type and any fully enclosed coarse voxels. Thevolume of either of the co-registered images of the first and secondcontrast can be used or a corresponding volume. The coarse segmentertransfer the results from one space through an appropriate transform.

A fine segmenter 32 grows a segmented region of voxels for eachcontrasted tissue type from the seed location based on a second growingalgorithm and an adjustable growing fraction for each segmented region.The segmented regions are bounded by the coarse segmented region grownfor the second contrasted tissue type. The seed location for the secondcontrast includes a first segmented region from the first contrast. Theadjustable growing fractions are initially the final values from thecorresponding final coarse growing fractions.

An enclosed tissue identification module 34 identifies any fullyenclosed voxels in the first segmented region. The fully enclosed voxelsrepresent a third related tissue type such as necrotic tissue. In anoption, the healthcare practitioner selects with an input device aray-search in the users controls displayed by the display device. Theenclosed tissue identification module performs a ray search on nearlyenclosed voxels by the first segmented region. The region uses vectorsprojected in multiple directions and rotated to determine nearlyenclosed. For example, if vectors of a voxel projected in eightdirections, rotated 22.5° and projected in again in eight directions,encounter the first segment region, then the voxel is classified as thethird tissue type. The ray search option is useful for instances wherevital tumor tissue includes a thin rim of the first contrast which maynot be contrasted sufficiently for the growing algorithms to identify.

The final segmentation and tissue identification can be displayed and/orstored in a storage management system 35 such as a Picture Archiving andCommunication System (PACS), a Radiology Information System (RIS), andthe like. Multiple results can be displayed and/or stored such asmultiple seed locations or tumors, different growing parameters and/orsearch parameters, and the like.

The various segmenters or modules 30, 32, 34 are suitably embodied by anelectronic data processing device, such as the electronic processor orelectronic processing device 24 of the workstation 20, or by anetwork-based server computer operatively connected with the workstation20 by the network 18, or so forth. The user interface module 28 issuitably embodied by the workstation 20. Moreover, the disclosedsegmentation and tissue identification techniques are suitablyimplemented as a non-transitory storage medium storing instructions(e.g., software) readable by an electronic data processing device andexecutable by the electronic data processing device to perform thedisclosed segmentation and tissue identification techniques.

FIG. 2A-B schematically illustrates several examples of coarse voxelconstruction performed by the coarse segmenter. In a first example ofFIG. 2A, the coarse voxel is constructed from a cubic volume of voxelssuch as volume 3×3×3 mm. Each voxel includes a single intensity value.The coarse voxel is characterized by two intensity values: a minimum anda maximum of the intensities of all voxels aggregated to form the coarsevoxel. The two values of the coarse voxel define the range of values forthe aggregated voxels. These two values are different from an average oran interpolated value typically used in other approaches. The range ofvalues reduces the information lost in aggregation while improving thecomputational performance. The volume preferred includes an incrementalvolume of an integer number of voxels and fractional voxels are avoided.The volume can be otherwise sized than 3×3×3 mm, and additionally otherregularly dimensioned shapes such as rectangular prism, rectangularcuboid, and the like are contemplated.

In a second example of FIG. 2B, the coarse voxel is constructed from aslice of voxels. The slice of voxels can be represented as 2D pixels.For example, with a slice thickness of 5 mm, a pixel spacing of0.449×0.449 mm, and a slice gap of 1 mm, the coarse segmenter can createa coarse voxel with a 6×6 plane of pixels. The coarse voxel can beconstructed of any regularly dimensioned pattern which includes thecomplete 2D sliced volume.

FIG. 3 diagrammatically illustrates one example of the various segmentedvolumes and optional ray search. The coarse segmenter segments the firstcoarse region with the first contrast and the seed 37 within the firstcontrast which approximates the volume or area of vital tumor tissue.The seed 37 include a point within the first contrast. The first coarseregion includes coarse voxels which are fully enclosed and includevolumes or areas of necrotic tissue. For example, the first coarsesegmented region includes areas label A and C.

In the growing algorithm, initially the first coarse region includes thecoarse voxel of the seed. The maximum of the first coarse region is themaximum of the coarse voxel included. The minimum of the first coarseregion is the first growing fraction of the maximum of the first coarseregion. For example, with a growing fraction of 0.6 and a maximum of100, the minimum of the first coarse region is 60. Neighboring coarsevoxels are searched and added to the first coarse region if the maximumof the coarse voxel searched exceeds the minimum of the first coarseregion. If the maximum of the coarse voxel added exceeds the maximum ofthe first coarse region, then the maximum of the first coarse regionbecomes the maximum of the added coarse voxel, a new minimum iscalculated based on the growing fraction, and the region growing isrestarted with the location of the new maximum as seed. If a coarsevoxel is added to the first coarse region and the minimum value of thecoarse voxel is less than the minimum value of the first coarse region,then the coarse voxel added is excluded for the search space ofneighboring regions. The instance of the coarse voxel minimum less thanthe minimum of the first coarse region suggests that some voxels do notinclude the tissue type of the first contrast. The growing algorithmiteratively searches neighboring regions, adds coarse voxels adaptingthe upper and lower bounds until no more coarse voxels are available tobe added. When the growing algorithm completes an iteration, the firstcoarse region is checked for inclusion of the seed. If the first coarseregion does not contain the seed, then the growing fraction isincremented, e.g. starting with 0.4 and incrementing 0.05. Notcontaining the seed in the first coarse region grown indicates thatleakage occurred. The growing restarts with the original seed and theincremented growing fraction. The algorithm ends once the first coarseregion grown contains the original seed or a maximum growing fraction isreached such as 0.7. The coarse segmenter 30 is made computationallyefficient by using coarse voxels. The use of coarse voxels as disclosedalso avoids leakage due to small connecting bright bands containingunrelated neighboring structures.

In the illustrative example of tumor segmentation, it is desired tosegment both the vital tumor and its surrounding edema. To accomplishthis, the coarse segmenter 30 is applied again to segment a secondcoarse region beginning with the areas A and C (the coarse segmentationof the vital tissue A and enclosed necrotic tissue C) and extending toinclude approximately the area labeled B (corresponding to the edema).Since the edema is expected to surround the vital tissue which in turnis expected to enclose any necrotic tissue that may be present, theareas A and C suitably serve as the seed for the segmentation of thesecond coarse segmented region (i.e., the edema). The second coarsesegmented region of areas A-C is grown using the same algorithm as thefirst coarse region except for a different seed and a second growingfraction. Also, in the illustrative example the first segmentation isperformed in a T1CE image that provides hyper-intense contrast for thevital tissue, while the second segmentation is performed in a FLAIRcontrast image which provides hyper-intense contrast for the edema. Inthe second segmentation, the growing fraction is for instance initiallyset to 0.35 and incremented in 0.05 increments. Fully enclosed coarsevoxels are not added to the second coarse segmented region. The secondcoarse region defines the boundary of the growing algorithm used by thefine segmenter. For example, the area D beyond the second coarse regionbecomes a masked region for the fine segmenter.

The coarse segmentation performed by the coarse segmenter 30 is followedby finer (i.e. higher resolution) segmentation performed by the finesegmenter 32. The fine segmenter refines the first coarse region to thefirst segmented region with a second growing algorithm, the originalseed, an adjustable growing fraction, and the second coarse region as aboundary or mask for growth. Growth during fine segmentation is by voxelrather than by coarse voxel (as was the case in coarse segmentation). Avoxel has only one value.

The fine segmenter 32 grows the first segmented region from the originalseed to a refined or first segmented region, which is expected to be ahigher resolution segmentation of the area A. A first adjustable growingfraction sets the lower bound for the growing algorithm and is initiallyset to the final growing fraction of the first coarse region. Thesegmented region begins with the original seed with a maximum valueequal to the intensity of the seed voxel and a minimum value equal tothe first adjustable growing fraction of the maximum value. Voxels in aneighboring region to the segmented region are searched, wherein theneighboring region excludes any voxels outside the second coarse regionor in the masked region B. Searched voxels are added to the segmentedregion if the value of the searched voxel exceeds the minimum value ofthe segmented region. For voxels added to the segmented region, themaximum of the segmented region is revised to include the greater of themaximum of the added voxel and the segmented region, and the minimum ofthe segmented region is revised as a growing fraction of the maximum ofthe segmented region. If the maximum is changed, the region growing isrestarted with the location of the new maximum as seed. The process ofsearching and adding voxels is repeated until no more voxels areavailable to be added to the segmented region.

The enclosed region identification module 34 identifies the enclosedarea and (if selected) any nearly enclosed areas. The enclosed areasinclude voxels fully enclosed by voxels in the first segmented area. Ifa ray search is selected, then a ray search is used to discover andidentify voxels nearly enclosed by the first segmented region such asthe region labeled E.

In similar fashion, the fine segmenter 32 grows the second segmentedregion (e.g., in the FLAIR image) to segment the edema starting from aseed which includes the first segmented region, the fully enclosedvoxels, and if identified, the nearly enclosed voxels. A secondadjustable growing fraction is used initially set to the final secondgrowing fraction. The boundary or mask for the second segmented regionis the second coarse region. The second adjustable growing fraction setsthe lower bound for the growing algorithm. The segmented region beginswith the first segmented region as seed with a maximum value equal tothe intensity of the seed voxels and a minimum value equal to the secondadjustable growing fraction of the maximum value. Voxels in aneighboring region to the segmented region are searched, wherein theneighboring region excludes any voxels outside the second coarse regionor in the masked region D. Searched voxels are added to the segmentedregion if the value of the searched voxel exceeds the minimum value ofthe segmented region. For voxels added to the segmented region, themaximum of the segmented region is revised to include the greater of themaximum of the added voxel and the segmented region, and the minimum ofthe segmented region is revised as the second adjustable growingfraction of the maximum of the segmented region. The process ofsearching and adding voxels is repeated until no more voxels are addedto the segmented region.

The healthcare practitioner preferably reviews the segmented regions,and through the user interface optionally modifies the adjustablegrowing fractions and/or search ray. If such manual adjustments aremade, then the fine segmenter 32 uses the modified adjustable growingfractions and/or search ray to grow new regions with the secondalgorithm, the original seed, the adjustable growing fractions, and thesecond coarse region as the mask.

FIG. 4 illustrates an example user interface displayed by the displaydevice. The user interface module creates the display and processes theinput relative to the display from the one or more input devices. Thehealthcare practitioner interacts with the user interface module throughthe user interface, display device, and input device to fine tune theregions segmented by the second growing algorithm.

The user interface module includes selection of data sets and/orspecific images for segmentation 48 based on standard conventions. Theselection can include a 3D volume or a 2D slice of a 3D volume. Imagecontrols 50 allow the user to manipulate the images. For example, aslider bar control can move the display through the image volume orbetween slices, a transparency control can permit image overlays, aradio button can permit selection of the manipulated imaging data set.The selection can include a single image with multiple contrasts,multiple images with single contrast, or multiple images with multiplecontrasts.

The healthcare practitioner identifies a specific location in onedisplayed image 46 and through one of the input devices indicates theseed. The seed selection includes a point of hyper-intensity. The imageis re-displayed with the segmented and identified regions contrastedsuch as a color enhancement for each segmented and identified region.For example, vital tumor tissue is displayed in red, edema tissuedisplayed in green, and necrotic tissue displayed in blue. With theimage re-display, a growing fraction control 52, 54 for each segmentedregion is displayed. The displayed growing fraction controls can includeslider bars, etc. and be normalized. For example, the adjustable growingfraction in current use is displayed as 0 and centered in the sliderbar. The healthcare practitioner moves the slider bar left or right todecrease or increase the size of the region grown. A movement to theleft or decrease in region size increases the adjustable growingfraction for the contrast. Similarly, a movement to the right can beindicative of desire to increase the growing region, and the adjustablegrowing fraction is correspondingly decreased. Each segmented regionincludes a separate user control for the adjustable growing fraction forthe region.

The optional ray search can be turned on and off by the healthcarepractitioner such as clicking to check or uncheck a checkbox 56. Theturned on ray search identifies nearly enclosed tissue (for example, thenearly enclosed region E in FIG. 3) and classifies the nearly enclosedtissue such as necrotic tissue. The identified nearly enclosed tissue isadded to the seed location for the second segmented region.

The user interface saves the final diagnostic image which includes thesegmented regions and identified tissue types. The saved informationincludes the same geometry as the image contrasts, but each voxel in asegmented region or identified tissue type includes a intensity value ofthe segmented or identified tissue type such as 0 for background, 1365for necrosis, 2730 for edema, and 4095 for vital tumor tissue. Thehealthcare practitioner can select another area indicative of a tumorand a new set of regions are grown with the selected area as the seedlocation.

With reference back to FIG. 1 and with further reference to FIG. 5, onemethod of using an embodiment of the segmentation and tissueidentification is flowcharted. The imaging data sets are selected in astep 60. The imaging data sets can include two images each with aseparate contrast such as a T1CE image contrasting vital tumor tissue,and a FLAIR image contrasting edema tissue. In a step 62, the healthcarepractitioner moves through the image or images and selects a seedlocation which indicates a tumor.

In a step 64, the coarse segmenter 30 iteratively grows the first coarsesegmented region using the first growing algorithm and the first growingfraction. The processor performing the coarse segmentation can betransparent to the healthcare practitioner. In a step 66, the coarsesegmenter adds the fully enclosed coarse voxels to the first coarsesegmented region. This first coarse segmentation is suitably performedin the T1CE image and provides coarse delineation of the vital tissue.

The coarse segmenter, in a step 68, similarly grows the second coarsesegmented region. The seed for the second coarse region includes thefirst coarse region. The first growing algorithm operates with thesecond growing fraction to grow the second coarse region. The secondcoarse region becomes the mask for the fine segmenter. The second coarsesegmentation is suitably performed in a FLAIR image and provides coarsedelineation of the edema.

The fine segmenter 32, in a step 70, grows the first segmented regionwith the original seed, the first adjustable growing fraction initiallyset to the final first growing fraction, and the second growingalgorithm. The first segmented region includes the first tissue typesuch as vital tumor tissue. This first fine segmentation is suitablyperformed in the T1CE image and provides fine delineation of the vitaltissue.

The enclosed tissue identification module 34, in a step 72, identifiesany voxels fully enclosed by the first segmented region. These enclosedregions are expected to correspond to necrotic tissue. If the ray searchis selected as indicated in a decision step 74, then any voxels nearlyenclosed by the first segmented region are identified in a step 76. Thefully enclosed voxels, and if selected, the nearly enclosed voxelsinclude the identified tissue type such as necrotic tissue.

In a step 78, the fine segmenter grows the second segmented region withthe seed including the first segmented region and any identified tissuetype. The fine segmenter grows the second segmented region with theseed, the second adjustable growing fraction initially set to the finalsecond growing fraction, the second growing algorithm, and the secondcoarse segmented region as the mask or boundry for growth. The secondfine segmentation is suitably performed in a FLAIR image and providesfine delineation of the edema.

The results output by the fine segmenter 32 and the enclosed tissueidentification module 34 are displayed by the user interface module 28in a step 80. The segmented regions and identified tissue types aredisplayed with visually perceptible delineations such as with differentcolors. The healthcare practitioner can review the regions segmentedand/or identified with the user controls, and optionally change theadjustable growing fractions or use of the ray search. Changes made bythe healthcare practitioner through the user interface are interpretedin a step 82 and restart the process beginning with the finesegmentation of the first segmented region. Alternatively the healthcarepractitioner can store the results in the storage management system in astep 84. Another alternative includes selection of a different seedlocation in a step 86 which restarts the process with selecting the seedlocation, followed by coarse segmentation.

With reference to FIG. 6, the algorithm for coarse segmentationimplemented by the coarse segmenter 30 is flowcharted. In a step 88, thegrowing region or coarse segmented region is initialized to the seed.The search region is initialized. An initial growing fraction isselected based on the image type, default value, and the like. In a step90, the neighboring coarse voxels are searched.

If a coarse voxel is found in a step 92 which includes a maximum whichexceeds the minimum for the coarse region segmented thus far, then thecoarse voxel is added to the coarse region in a step 94. The minimum ofthe coarse voxel to be added is compared in a step 92. If the coarsevoxel minimum is lower than the minimum for the coarse region, then in astep 94, the coarse voxel is excluded from the search criteria. In astep 96, the maximum of the coarse voxel to be added is compared withthe maximum of the coarse region segmented thus far. If the maximum ofthe coarse voxel to be added exceeds the maximum of the coarse region,then the coarse region maximum is set to the maximum of the coarse voxelto be added in a step 98. If the maximum of the coarse region ischanged, then the minimum is computed as the maximum times the growingfraction in a step 100, and the region growing is restarted with thecoarse region reinitialized to the location of the new maximum as theseed in a step 102. The process repeats from the search of neighboringcoarse voxels 90 until all coarse voxels are added.

If no more coarse voxels are available to be added, then in a step 104,the coarse segmented region is inspected to verify inclusion of theseed. If the seed is not contained in the coarse segmented region, thenthe growing fraction is incremented in a step 106, and the segmentedregion and search region reset to the seed in a step 108. Afterincrementing the growing fraction and resetting the seed, the algorithmregrows the segmented region beginning with the search of neighboringcoarse voxels. The coarse segmented region is iteratively regrownincrementing the growing fraction until the seed is contained in thecoarse segmented region or the growing fraction reaches a maximum suchas 0.70. The final coarse segmented region and the final growingfraction is returned 110.

With reference to FIG. 7, the algorithm for fine segmentation isflowcharted. In a step 112, the segmented region is initialized and theadjustable growing fraction input. The adjustable growing fraction isset to the final corresponding growing fraction unless modified by thehealthcare practitioner. The neighboring voxels are searched in a step114. Voxels outside the second coarse segmented region are excluded fromthe search. The algorithm continues if voxels are available to be addedin a step 116, otherwise the segmented region is returned 128.

Voxels of an intensity which exceeds the minimum of the segmented regionare included in the segmented region in a step 118. The intensity voxelto be added is compared with the maximum of the segmented region in astep 120, and if the voxel intensity exceeds the maximum of thesegmented region before adding, then in a step 122 the maximum of theregion is updated to the voxel intensity, and a new minimum of thesegmented region or growing region is computed in a step 124 as theregion maximum times the adjustable growing fraction. All voxels foundin the search are added, and the process is repeated beginning with thesearch until no voxels are added to the segmented region. The segmentedregion is returned.

The illustrative examples pertain to segmenting a tumor expected tocomprise vital tumor tissue (possibly containing some necrotic regions)surrounded by an edema. To segment both the vital tissue and edema, thecoarse and fine segmentation processes are each run twice, with thesecond run using the vital tissue delineated by the first run as theseed. In some other applications, no outer structure corresponding tothe edema may be present—in such cases, a single coarse region growingprocess is followed by a single fine region growing process (the latteroptionally repeated to include any received user adjustments). Theillustrative examples employ a T1CE image to provide hyper-intensecontrast for vital tumor tissue, and a FLAIR image to providehyper-intense contrast for the edema. In some other applications, asingle image may provide both contrasts in which case the vital tumortissue and edema tissue segmentations may be performed on the singleimage. As yet another contemplated variation, while in the illustrativeembodiments the region growing fraction and maximum intensity are usedas tunable parameters for adjusting the coarse segmentation to avoidleakage, it is additionally or alternatively contemplated to adjust thesize and/or shape of the coarse voxels for this purpose.

It is to be appreciated that in connection with the particularillustrative embodiments presented herein certain structural and/orfunction features are described as being incorporated in definedelements and/or components. However, it is contemplated that thesefeatures may, to the same or similar benefit, also likewise beincorporated in other elements and/or components where appropriate. Itis also to be appreciated that different aspects of the exemplaryembodiments may be selectively employed as appropriate to achieve otheralternate embodiments suited for desired applications, the otheralternate embodiments thereby realizing the respective advantages of theaspects incorporated therein.

It is also to be appreciated that particular elements or componentsdescribed herein may have their functionality suitably implemented viahardware, software, firmware or a combination thereof. Additionally, itis to be appreciated that certain elements described herein asincorporated together may under suitable circumstances be stand-aloneelements or otherwise divided. Similarly, a plurality of particularfunctions described as being carried out by one particular element maybe carried out by a plurality of distinct elements acting independentlyto carry out individual functions, or certain individual functions maybe split-up and carried out by a plurality of distinct elements actingin concert. Alternately, some elements or components otherwise describedand/or shown herein as distinct from one another may be physically orfunctionally combined where appropriate.

In short, the present specification has been set forth with reference topreferred embodiments. Obviously, modifications and alterations willoccur to others upon reading and understanding the presentspecification. It is intended that the invention be construed asincluding all such modifications and alterations insofar as they comewithin the scope of the appended claims or the equivalents thereof. Thatis to say, it will be appreciated that various of the above-disclosedand other features and functions, or alternatives thereof, may bedesirably combined into many other different systems or applications,and also that various presently unforeseen or unanticipatedalternatives, modifications, variations or improvements therein may besubsequently made by those skilled in the art which are similarlyintended to be encompassed by the following claims.

1. A medical imaging system, comprising: a workstation including: atleast one input which receives a selected location in an imaging dataset having different contrast for a first and a second contrasted tissuetype as a seed in the first contrasted tissue type; and a display whichdisplays a diagnostic image delineating a first segmented region of thefirst tissue type and a second segmented region of the second contrastedtissue type and identified regions which include regions fully enclosedby the first segmented region as a third tissue type; a coarsesegmenting processor programmed to perform a first segmenting operationincluding growing a coarse segmented region of coarse voxels for eachcontrasted tissue type from the seed location based on a first growingalgorithm and a growing fraction for each contrasted tissue type,wherein the seed location for growing the second contrasted tissue typeincludes the first coarse segmented region and any fully enclosed coarsevoxels, and each coarse voxel includes an aggregation of voxels and amaximum and a minimum of the voxel intensities; a fine segmentingprocessor programmed to perform a second segmenting operation includinggrowing a segmented region of voxels for each contrasted tissue typefrom the seed location and bounded by the second coarse segmented regionbased on a second growing algorithm and an adjustable growing fractionfor each contrasted tissue type initially set to the growing fractionfor the corresponding region, wherein the seed location for growing thesecond contrasted tissue type includes the first segmented region andany identified regions; and an enclosed tissue identification processorprogrammed to identify any regions of voxels fully enclosed by the firstsegmented region as being of the third tissue type; wherein the coarsesegmenting processor, the fine segmenting processor, and the enclosedtissue identification processor are implemented by an electronic dataprocessor.
 2. The medical imaging system according to claim 1, whereinthe coarse segmenting processor is configured to grow the coarsesegmented region by: searching the coarse voxels in a region neighboringthe coarse segmented region; and adding each searched coarse voxel tothe coarse segmented region if the maximum value of the searched coarsevoxel exceeds the minimum value of the coarse segmented region; and whenadding the searched coarse voxel to the coarse segmented region:updating the maximum of the coarse segmented region to the larger of themaximum of the added coarse voxel and the maximum of the coarsesegmented region; if the maximum is the maximum of thew added coursevoxel, then updating the minimum of the coarse segmented region as thegrowing fraction of the updated maximum of the coarse segmented regionand restarting the region growing with the location of the new maximumas the seed; and revising the search to exclude added coarse voxels withminimum values lower than the updated minimum value of the coarsesegmented region.
 3. The medical imaging system according to claim 1,wherein the fine segmenting processor is configured to grow thesegmented region by: searching voxels in a region neighboring thesegmented region, wherein the neighboring region excludes voxels outsidea bounded region defined by the coarse segmenting processor; adding eachsearched voxel to the segmented region if the value of the searchedvoxel exceeds the minimum value of the segmented region; and when addingthe searched voxel to the segmented region: updating the maximum of thesegmented region to include the greater of the value of the added voxeland the maximum value of the segmented region; and revising the minimumof the segmented region as the adjustable growing fraction of theupdated maximum of the segmented region.
 4. The medical imaging systemaccording to any claim 1, wherein: the display further displays userselectable controls for input of the adjustable growing fraction foreach segmented region; wherein the fine segmenting processor regrows thesegmented regions based on the input adjustable growing fraction foreach segmented region.
 5. The medical imaging system according to claim1, wherein the first contrasted tissue type includes vital tumor tissueand the second contrasted tissue type includes edema tissue and thethird tissue type include necrotic tissue and the displayed diagnosticimage contrasts the tissue types.
 6. The medical imaging systemaccording to claim 1, wherein the enclosed tissue identificationprocessor is configured to: search for regions nearly enclosed by thesegmented region of the first contrasted tissue type based on a raysearch and identify any nearly enclosed regions as the third tissuetype.
 7. The medical imaging system according to claim 1, wherein theseed location has the contrast indicative of a tumor.
 8. The medicalimaging system according to claim 1, wherein the at least one imagingdata set includes at least one of: a T1 contrast enhanced magneticresonance image; a fluid attenuated inversion recovery magneticresonance image; and a positron emission tomography image.
 9. Themedical imaging system according to claim 1, wherein the at least oneimaging data set includes: a T1 contrast enhanced magnetic resonanceimage wherein the first segmented region of the first tissue typecomprises a first segmented region of vital tumor tissue; and a fluidattenuated inversion recovery magnetic resonance image wherein thesecond segmented region of the second tissue type comprises a secondsegmented region of edema tissue.
 10. The medical imaging systemaccording to claim 1, wherein the at least one imaging data set includesmulti-contrast magnetic resonance brain images.
 11. A method comprising:performing a coarse region growing algorithm to define a coarsesegmented region in an image, the coarse region growing algorithmoperating on coarse voxels of the image where each coarse voxel is anaggregation of voxels characterized in the coarse region growingalgorithm by the maximum voxel value of the aggregation of voxels andthe minimum voxel value of the aggregation of voxels; performing a fineregion growing algorithm to define a segmented region in the image, thefine region growing algorithm operating on voxels of the image and beingconstrained by a growth bound defined by the coarse segmented region;and displaying the segmented region on a display; wherein the performingoperations are performed by an electronic data processor.
 12. The methodof claim 11, wherein: the coarse region growing algorithm adds a coarsevoxel to the coarse segmented region if the maximum voxel value of thecoarse voxel exceeds a threshold voxel value; and the coarse regiongrowing algorithm uses the added coarse voxel for further growth of thecoarse segmented region if the minimum voxel value of the coarse voxelexceeds the threshold voxel value.
 13. The method of claim 12, whereinthe coarse region growing algorithm computes the threshold voxel valueas a selected fraction of the maximum of the maximum voxel values ofcoarse voxels making up the coarse segmented region.
 14. The method ofclaim 11, wherein the image includes a T1 contrast enhanced (T1CE)magnetic resonance image and a fluid attenuated inversion recovery(FLAIR) magnetic resonance image, and the method comprises: performingthe coarse region growing algorithm on the T1CE image to coarselydelineate vital tumor tissue; performing the coarse region growingalgorithm on the FLAIR image to coarsely delineate edema tissue;performing the fine region growing algorithm on the T1CE image todelineate vital tumor tissue constrained by a boundary defined by thecoarsely delineate edema tissue; and performing the fine region growingalgorithm on the FLAIR image to delineate edema tissue constrained by aboundary defined by the coarsely delineate edema tissue.
 15. The methodof claim 14, wherein: the performing of the coarse region growingalgorithm on the FLAIR image uses a seed comprising the coarselydelineated vital tumor tissue; and the performing of the fine regiongrowing algorithm on the FLAIR image uses a seed comprising thedelineated vital tumor tissue.
 16. The method of claim 14, furthercomprising: identifying any regions of voxels fully enclosed by thedelineated vital tumor tissue as being of necrotic tissue, theidentifying being performed by an electronic data processing device. 17.A non-transitory computer-readable medium carrying software whichcontrols one or more electronic data processing devices to perform amethod as set forth in claim
 11. 18. An electronic data processorconfigured to perform a method as set forth in claim
 11. 19. A medicalimaging system comprising an electronic data processor programmed toperform operations including: segmenting a T1 contrast enhanced (T1CE)magnetic resonance image of an imaging subject to define a segmentedregion comprising vital tumor tissue; and segmenting a fluid attenuatedinversion recovery (FLAIR) magnetic resonance image of the imagingsubject using region growing to define a segmented region comprisingedema tissue wherein the segmented region comprising vital tumor tissueserves as the seed for the region growing.
 20. The medical imagingsystem of claim 1, wherein the electronic data processor is furtherprogrammed to: perform the first and second segmenting operations afirst time operating on coarse voxels of the T1 contrast enhanced andfluid attenuated inversion recovery images wherein a coarse voxel is anaggregation of voxels characterized by the maximum voxel value of theaggregation of voxels and the minimum voxel value of the aggregation ofvoxels; and perform the first and second segmenting operations a secondtime operating on voxels of the T1 contrast enhanced and fluidattenuated inversion recovery images wherein the segmented regioncomprising edema tissue defined by the first segmenting operationperformed on the fluid attenuated inversion recovery image serves as aconstraining boundary for the second segmenting operations.